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Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping
Geomatics, Natural Hazards and Risk ( IF 4.2 ) Pub Date : 2020-03-28
Thimmaiah Gudiyangada Nachappa, Stefan Kienberger, Sansar Raj Meena, Daniel Hölbling, Thomas Blaschke

Abstract

Remote sensing and geographic information systems (GIS) are widely used for landslide susceptibility mapping (LSM) to support planning authorities to plan, prepare and mitigate the consequences of future hazards. In this study, we compared the traditional per-pixel models of data-driven frequency ratio (FR) and expert-based multi-criteria assessment, i.e. analytical hierarchical process (AHP), with an object-based model that uses homogenous regions (‘geon’). The geon approach allows for transforming continuous spatial information into discrete objects. We used ten landslide conditioning factors for the four models to produce landslide susceptibility maps: elevation, slope angle, slope aspect, rainfall, lithology, geology, land use, distance to roads, distance to drainage, and distance to faults. Existing national landslide inventory data were divided into training (70%) and validation data (30%). The spatial correlation between landslide locations and the conditioning factors were identified using GIS-based statistical models. Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the resulting susceptibility maps. The area under the curve (AUC) was used to obtain the following values from ROC for the per-pixel based FR approach (0.894) and the AHP (0.886) compared with the object-based geon FR approach (0.905) and the geon AHP (0.896). The object-based geon aggregation yielded a higher accuracy than both per-pixel based weightings (FR and AHP). We proved that the object-based geon approach creates meaningful regional units that are beneficial for regional planning and hazard mitigation.



中文翻译:

基于像素和基于对象的滑坡敏感性地图绘制方法的比较和验证

摘要

遥感和地理信息系统(GIS)被广泛用于滑坡敏感性地图(LSM),以支持规划部门计划,准备和减轻未来灾害的后果。在这项研究中,我们将数据驱动频率比(FR)和基于专家的多标准评估(即分析层次过程(AHP))的传统每像素模型与使用同质区域的基于对象的模型进行了比较( geon')。几何方法允许将连续的空间信息转换为离散的对象。我们使用四个模型的十个滑坡条件因子来生成滑坡敏感性图:高程,坡度角,坡度,降雨,岩性,地质,土地利用,到公路的距离,到排水的距离以及到断层的距离。现有的国家滑坡清查数据分为培训(70%)和验证数据(30%)。使用基于GIS的统计模型确定滑坡位置与条件因子之间的空间相关性。接收器的工作特性(ROC)和相对滑坡密度指数(R-index)被用来验证最终的磁化率图。对于基于像素的FR方法(0.894)和AHP(0.886),与基于对象的geon FR方法(0.905)和geon AHP相比,曲线下面积(AUC)用于从ROC中获得以下值(0.896)。与基于像素的权重(FR和AHP)相比,基于对象的Geon聚合产生的精度更高。

更新日期:2020-04-20
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